Sigang Yu

Orcid: 0000-0002-3004-5395

According to our database1, Sigang Yu authored at least 12 papers between 2022 and 2024.

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Bibliography

2024
MEET: A Multi-Band EEG Transformer for Brain States Decoding.
IEEE Trans. Biomed. Eng., May, 2024

2023
MM-GLCM-CNN: A multi-scale and multi-level based GLCM-CNN for polyp classification.
Comput. Medical Imaging Graph., September, 2023

Differentiating brain states via multi-clip random fragment strategy-based interactive bidirectional recurrent neural network.
Neural Networks, August, 2023

An explainable deep learning framework for characterizing and interpreting human brain states.
Medical Image Anal., 2023

Review of Large Vision Models and Visual Prompt Engineering.
CoRR, 2023

Prompt Engineering for Healthcare: Methodologies and Applications.
CoRR, 2023

Joint Representation of Functional and Structural Profiles for Identifying Common and Consistent 3-Hinge Gyral Folding Landmark.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

Exploring Brain Function-Structure Connectome Skeleton via Self-supervised Graph-Transformer Approach.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

A Novel Multi-Modality Framework for Exploring Brain Connectivity Hubs Via Reinforcement Learning Approach.
Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023

A Diffusion-Based Multi-Objective Ant Colony Algorithm for Optimizing Network Topology Design.
Proceedings of the 9th International Conference on Communication and Information Processing, 2023

2022
A Bagging Strategy-Based Multi-scale Texture GLCM-CNN Model for Differentiating Malignant from Benign Lesions Using Small Pathologically Proven Dataset.
Proceedings of the Multiscale Multimodal Medical Imaging - Third International Workshop, 2022

A Novel Two-Stage Multi-view Low-Rank Sparse Subspace Clustering Approach to Explore the Relationship Between Brain Function and Structure.
Proceedings of the Machine Learning in Medical Imaging - 13th International Workshop, 2022


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